Decision-making criteria for AI tools in digital education

Artificial intelligence (AI) technologies in education have great potential, but choosing the right ones necessitates using well-informed selection criteria. Drawing on studies over the last five years, this review investigates important factors to consider when educators choose AI tools. The impact on motivation and knowledge enhancement using quasi-experimental approaches, prediction accuracy utilizing machine learning models and cross-validation procedures, and algorithm performance (e.g., accuracy, precision, recall) are some of the key criteria that were discovered. Fairness, transparency, and gender prejudice are important ethical considerations that call for creating policy frameworks to reduce bias and uphold ethical integrity. Along with concerns about educational equity and the caliber of AI-generated content for tailored learning experiences, transparency in AI operations is found to be essential for acceptability. The analysis highlights prospect to improve educational results while addressing ethical and practical constraints by synthesizing studies to emphasize the systematic evaluation required for AI tool use in education.